Note 1: Covid19 Data at March 29 2020 [1], expected cases extracted from GPW2020 [2]. Bayesian spatial models from [4-6].

Our risk data can downloaded from here.

INLA Model

Cases (30-03-2020)

Exploring the risk

Relative risk maps generated with INLA.

Vulnerability evaluation

There are no P values in Bayes. Importance or significance of variables can be deduced by examining the overlap of their 2.5% and 97.5% posterior estimates with zero.

                         mean 0.025quant 0.975quant
(Intercept)              0.43       0.38       0.48
minorities               0.89       0.68       1.16
transportTypeairport     1.27       1.02       1.59
transportTypemainRoad    0.72       0.62       0.83
transportTypenextAirport 0.78       0.67       0.90
Loading required package: MCMCglmm
Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
logical.return = TRUE, : there is no package called 'MCMCglmm'

FINDINGS

REFERENCES

[1] Data from The New York Times, based on reports from state and local health agencies. https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html.

[2] Center for International Earth Science Information Network - CIESIN - Columbia University, United Nations Food and Agriculture Programme - FAO, and Centro Internacional de Agricultura Tropical - CIAT. 2005. Gridded Population of the World, Version 4 (GPWv4.11): Population Count Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4639MPP. Accessed 21 03 2020.

[3] ACS County-to-County Migration Flows 2013-2017. https://www.census.gov/topics/population/migration.html

[4] CDC SVI 2018 Documentation, 1/31/2020. https://svi.cdc.gov/Documents/Data/2018_SVI_Data/SVI2018Documentation.pdf

[5] Moraga, Paula. (2019). Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Chapman & Hall/CRC Biostatistics Series.

[6] Spatial and spatio-temporal models with R-INLA. M Blangiardo, M Cameletti, G Baio, H Rue. Spatial and spatio-temporal epidemiology 4, 33-49.

[7] Flanagan, Barry E.; Gregory, Edward W.; Hallisey, Elaine J.; Heitgerd, Janet L.; and Lewis, Brian (2011) “A Social Vulnerability Index for Disaster Management,” Journal of Homeland Security and Emergency Management: Vol. 8: Iss. 1, Article 3. DOI: 10.2202/1547-7355.1792

[8] Jung I (2009). “A Generalized Linear Models Approach to Spatial Scan Statistics for Covariate Adjustment.” Statistics in Medicine, 28(7), 1131–1143. doi:10.1002/sim.3535.

[9] Zhang T, Lin G (2009). “Spatial Scan Statistics in Loglinear Models.” Computational Statistics & Data Analysis, 53(8), 2851–2858. doi:10.1016/j.csda.2008.09.016.

[10] Kulldorff M (1997). “A Spatial Scan Statistic.” Communications in Statistics – Theory and Methods, 26(6), 1481–1496. doi:10.1080/03610929708831995.

[11] Waller LA, Gotway CA (2004). Applied Spatial Statistics for Public Health Data. John Wiley & Sons. doi:10.1002/0471662682.

[12] Gómez-Rubio V, Moraga P, Molitor J (2018). “Fast Bayesian Classification for Disease Mapping and the Detection of Disease Clusters.” In M Cameletti, F Finazzi (eds.), Quantitative Methods in Environmental and Climate Research, pp. 1–27. Springer-Verlag. doi: 10.1007/978-3-030-01584-8_1.

[13] Gómez-Rubio, V., Moraga, P., Molitor, J., & Rowlingson, B. (2019). DClusterm: Model-Based Detection of Disease Clusters. Journal of Statistical Software, 90(14), 1 - 26. doi:http://dx.doi.org/10.18637/jss.v090.i14.